初选中的比例排名：案例研究
Proportional Ranking in Primary Elections: A Case Study
摘要：
许多民主政党举行初选，这很好地反映了它们的民主性质，并除其他外促进了包容性的民主价值。然而，目前用于举行这种初选的方法可能不是最合适的，特别是如果需要某种形式的比例排名。在本文中，我们比较了用于保持初选的不同算法方法（即，不同的选民选票汇总方法），通过使用真实世界的数据评估每种方法所实现的比例排名程度。特别是，我们比较了六种不同的算法，通过分析现实世界的数据，从最近的初选由以色列民主党。从技术上讲，我们分析独特的选民数据，并通过聚类分析来评估所实现的比例，旨在确定在所考虑的每种算法方法下授予不同选民群体的代表性。我们的发现表明，与所使用的最突出的原色算法（即，批准），其他方法，如顺序比例批准或Phragmen可以带来更好的比例排名，因此可能更适合在实践中的初选。
英文摘要：
Many democratic political parties hold primary elections, which nicely reflects their democratic nature and promote, among other things, the democratic value of inclusiveness. However, the methods currently used for holding such primary elections may not be the most suitable, especially if some form of proportional ranking is desired. In this paper, we compare different algorithmic methods for holding primaries (i.e., different aggregation methods for voters' ballots), by evaluating the degree of proportional ranking that is achieved by each of them using real-world data. In particular, we compare six different algorithms by analyzing real-world data from a recent primary election conducted by the Israeli Democratit party. Technically, we analyze unique voter data and evaluate the proportionality achieved by means of cluster analysis, aiming at pinpointing the representation that is granted to different voter groups under each of the algorithmic methods considered. Our finding suggest that, contrary to the most-prominent primaries algorithm used (i.e., Approval), other methods such as Sequential Proportional Approval or Phragmen can bring about better proportional ranking and thus may be better suited for primary elections in practice.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
